A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute Prediction
نویسندگان
چکیده
منابع مشابه
A Binary Neural Network Framework for Attribute Selection and Prediction
In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus jour...
متن کاملClustered Multi-task Feature Learning for Attribute Prediction
Semantic attributes have been proposed to bridge the semantic gap between low-level feature representation and high-level semantic understanding of visual objects. Obtaining a good representation of semantic attributes usually requires learning from high-dimensional low-level features, which often suffers from the curse of dimensionality. Designing a good feature-selection approach would benefi...
متن کاملGeneralization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning
Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by incorporating deep neural networks in learning representations from the input to RL. However, the conventional deep neural network architecture is limited in learning representations for multi-task RL (MT-RL), as multiple tasks can refer to different kinds of representations. In this paper, we thus propose a novel deep...
متن کاملAn artificial Neural Network approach to monitor and diagnose multi-attribute quality control processes
One of the existing problems of multi-attribute process monitoring is the occurrence of high number of false alarms (Type I error). Another problem is an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, we address both of these problems and consider monitoring correlated multi-attributes proce...
متن کاملMtNet: A Multi-Task Neural Network for Dynamic Malware Classification
In this paper, we propose a new multi-task, deep learning architecture for malware classification for the binary (i.e. malware versus benign) malware classification task. All models are trained with data extracted from dynamic analysis of malicious and benign files. For the first time, we see improvements using multiple layers in a deep neural network architecture for malware classification. Th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2021
ISSN: 2157-6904,2157-6912
DOI: 10.1145/3418285